PyTorch for Deep Learning Bootcamp Course Syllabus

This course provides a hands-on introduction to deep learning using PyTorch, one of the most popular frameworks in AI. Designed for beginners, it guides you through building, training, and deploying neural networks using real-world datasets like MNIST and CIFAR-10. With a clear, project-driven structure, the course covers essential concepts from tensor operations to transfer learning, culminating in an end-to-end project that mirrors production workflows. Total time commitment is approximately 7 hours, making it ideal for learners looking to gain practical deep learning skills quickly and effectively.

Module 1: Introduction to Deep Learning and PyTorch

Estimated time: 0.5 hours

• Overview of AI and deep learning workflows
• Installing PyTorch and setting up the development environment
• Understanding the role of PyTorch in deep learning

Module 2: PyTorch Basics and Tensor Operations

Estimated time: 0.75 hours

• Understanding tensors and their properties
• Performing basic and advanced tensor operations
• Introduction to gradients and automatic differentiation
• Writing and debugging simple PyTorch programs

Module 3: Building Neural Networks

Estimated time: 1 hour

• Constructing feedforward neural networks from scratch
• Using nn.Module to define custom model classes
• Configuring layers, activations, and network architecture

Module 4: Training and Evaluation Loops

Estimated time: 1 hour

• Implementing training, validation, and testing loops
• Working with loss functions (e.g., CrossEntropyLoss)
• Using optimizers like SGD and Adam

Module 5: Convolutional Neural Networks (CNNs)

Estimated time: 1 hour

• Building CNNs for image classification tasks
• Applying convolutional and pooling layers
• Training CNNs on MNIST and CIFAR-10 datasets

Module 6: Transfer Learning and Fine-Tuning

Estimated time: 1 hour

• Leveraging pre-trained models like ResNet
• Understanding feature extraction vs. fine-tuning
• Adapting models for custom datasets and tasks

Module 7: Saving, Loading, and Inference

Estimated time: 0.75 hours

• Persisting trained models using torch.save()
• Loading models with torch.load()
• Performing inference on new data samples

Module 8: End-to-End Project

Estimated time: 1.25 hours

• Complete model development cycle: data preparation, model design, and training
• Evaluating performance on test data
• Applying best practices for deployment and monitoring

Prerequisites

• Basic understanding of Python programming
• Familiarity with NumPy arrays and operations
• No prior deep learning experience required

What You'll Be Able to Do After

• Understand core deep learning principles and their implementation in PyTorch
• Build, train, and evaluate neural networks from scratch
• Apply CNNs and transfer learning to real-world image classification tasks
• Save and load models for inference and deployment
• Follow best practices in developing production-ready deep learning workflows